




'''simple docstring'''



import os







def       _UpperCamelCase			()-> Union[str, Any]:




     '''simple docstring'''





     __snake_case            =	os.path.dirname(os.path.realpath(_lowerCamelCase						)						)
     __snake_case            =	os.path.join(_lowerCamelCase ,						'''triangle.txt'''						)

     with open(_lowerCamelCase						) as f:
          __snake_case            =	f.readlines()

     __snake_case            =	[]
     for line in triangle:
          __snake_case            =	[]
          for number in line.strip().split(''' '''						):
               numbers_from_line.append(int(_lowerCamelCase						)						)
          a.append(_lowerCamelCase						)

     for i in range(1 ,						len(_lowerCamelCase						)						):
          for j in range(len(a[i]						)						):
               __snake_case            =	a[i - 1][j] if j != len(a[i - 1]						) else 0
               __snake_case            =	a[i - 1][j - 1] if j > 0 else 0
               a[i][j] += max(_lowerCamelCase ,						_lowerCamelCase						)
     return max(a[-1]						)


if __name__ == "__main__":
     print(solution())










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCamelCase     		=							{
    'configuration_xlm_roberta_xl': [
        'XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'XLMRobertaXLConfig',
        'XLMRobertaXLOnnxConfig',
    ],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__UpperCamelCase     		=							[
						    'XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'XLMRobertaXLForCausalLM',
						    'XLMRobertaXLForMaskedLM',
						    'XLMRobertaXLForMultipleChoice',
						    'XLMRobertaXLForQuestionAnswering',
						    'XLMRobertaXLForSequenceClassification',
						    'XLMRobertaXLForTokenClassification',
						    'XLMRobertaXLModel',
						    'XLMRobertaXLPreTrainedModel',
						]

if TYPE_CHECKING:
						from .configuration_xlm_roberta_xl import (
						    XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    XLMRobertaXLConfig,
						    XLMRobertaXLOnnxConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_xlm_roberta_xl import (
												    XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
												    XLMRobertaXLForCausalLM,
												    XLMRobertaXLForMaskedLM,
												    XLMRobertaXLForMultipleChoice,
												    XLMRobertaXLForQuestionAnswering,
												    XLMRobertaXLForSequenceClassification,
												    XLMRobertaXLForTokenClassification,
												    XLMRobertaXLModel,
												    XLMRobertaXLPreTrainedModel,
												)

else:
						import sys

						__UpperCamelCase     		=							_LazyModule(__name__, globals()['__file__'], _import_structure)


